There is nothing new about business forecasting as, for centuries, the businessmen have tried to adjust themselves in such a manner as to make the best out of the future conditions. The rule-of-thumb method has been widely practiced in business. It consists of deciding about the future in terms of past experience and familiarity with the problem at hand even today. This method is very widely used in business however; it can lead to absurd conclusions if employed by the inexperienced.

A forecast us usually a combination of several technique.

Business barometers: - of great assistance in practical forecasting is a series that can be used index or indicator is also widely, though loosely, used in business statistics; sometimes the term is used to mean simply an indicator of the present economic situations and sometimes it is used to designate an indicator of future conditions.

The following are some of the important series which aid businessmen in forecasting:

(i) Gross national product

(ii) Employment

(iii) Wholesale prices

(iv) Consumer prices

(v) Industrial production

(vi) Volume of bank deposits and currency outstanding

(vii) Consumer credit

(viii) Disposable personal income

(ix) Departmental store sales

(x) Stock process

(xi) Bond yields.

Extrapolation:- extrapolation is the simplest yet often a useful method of forecasting. In many forecasting situation the most reasonable expectation is that the variable will follow its already established path. Extrapolation relies on the relative constancy in the pattern of past movements in some time series. Strictly speaking, nothing needs to be known about causation-why the series moves as it does. But in practice the justification does involve the nature of the growth process being described. Extrapolation is used frequently for sales forecasts and for other estimates when better forecasting methods may not be justified.

Most of the most useful ones are:

Arithmetic trend:- the straight-line arithmetic trend assumes that growth will be b a constant absolute amount each year.

Semi-log trend:- the semi-logarithmic trend assumes a constant percentage increase each year. Since the annual increment is constant in logarithms. This line translates into a straight line when drawn on paper within a logarithmic vertical scale.

Modified exponential trend:- this curve assumes that each increment of growth will be a constant per cent than 100 of the previous one. The line terns generally to approach, by never quite reach a distant asymptote, which may be thought of as an upper limit.

Regression analysis:- the regression approach offers many valuable contributions to the solution of the forecasting problem. It is the means by which we select form among the many possible or theoretically suggestive relationships between to variables to precise quantified knowledge. If possible an estimate of the other. For example. If we know that advertising expenditure and sales are correlated then for a given advertising expenditure. We can find out the probable increase in sales or vice versa.

Econometric models:- econometric techniques which originated in the eighteenth century, have recently gained in popularity for forecasting. Much or the revival of econometrics is attributed to the growth of computer technology. The term econometrics refers to the application of mathematical economic theory and statistical procedures to economic data in order to verify economic theorems and to establish quantitative results in economics. An all in the econometric models take the form of a set of simultaneous equations. The values of the constants in such equations are supplied by a study of statistical time series and a large number of equations may be necessary to produce an adequate model. The work of computations is greatly facilitated by electronic data processing equipment like computer etc.

Forecasting by the use of time series analysis:- time series analysis helps to identify and explain:

Any regular or systematic variation in the series of data which is due to seasonality-the seasonal.

Cyclical patterns

Trends in the data

Growth rates of these trends unfortunately; most existing methods indentify only the seasonal, the combined effect of trends and excises separate trend from cycles.

Opinion polling:- opinion poll is the survey of opinion of experts knowledgeable persons in the field whose views carry lot of weight, for example, a survey of opinion of sales representatives, wholesalers, retailers, etc. shall be of great help in formulating demand projections. The survey research centre of the University of Michigan conducts an annual pool regarding the future plans of consumers. The answers too many questions are translated into short-run demand for color television sets, automobiles and other consumer products.

Causal models: - a causal model is the most sophisticated king of forecasting tool. It expresses mathematic call the relevant causal relationships. And may include pipeline considerations (inventories) and market survey information. It may also directly incorporate the result fo a time series analysis.

Illustration: from the following values prepare forecasts by the methods of exponential smoothing taking initial estimate as 100, the values of α = 0.04 and an initial trend value zero:

Time period (ƒ)	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
Sales ($ crores)	104	108	118	115	120	122	123	125	128	130

Solution:

t	X1	Smoothed value St	Change in St ∆St	Trend estimate tt	Forecast St	Forecasting error (e)
1999	104	100.0	-	-	-	-
2000	108	103.2	3.20	1.28	105.12	-12.88
2001	118	109.12	5.92	3.14	113.83	-1.17
2002	115	111.47	2.35	2.82	115.70	-4.30
2003	120	114.83	3.41	3.06	119.47	-2.53
2004	122	117.73	2.85	2.98	122.20	-0.80
2005	123	119.84	2.11	2.63	123.79	-1.21
2006	125	121.90	2.06	2.40	125.50	-2.50
2007	128	124.34	2.44	2.42	127.97	-2.03
2008	130	126.60	2.26	2.36	130.14	-

Exponential smoothing:- this method is an outgrowth of the recent attempts to maintain the smoothing function of moving averages without their corresponding drawbacks and limitations. Exponential smoothing is a special kind of weighted average and is found extremely useful in short-term forecasting of inventories and sales.

Smoothing process: - the steps in smoothing process are:

The exponentially smoothed value at time period t is denoted by Si the smoothing process begins by assigning S₁ = X₁ at the first time period. For the second time period.

S₂ = a X₂ + β S₁

And for any succeeding time period t the smoothed value Si is found by computing.

S_t = a X_t + β S_t – 1

Survey method; - the survey method is very widely iced as a tool of forecasting for the existing any new products. Field surveys are conducted and the necessary information, both quantitative and qualitative obtained. Forecasts are made out about likely demand expenditure on consumer durables. Etc. attitude of consumers about consumption of different items provides very useful information.

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